{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#                                           numpy 练习题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy 的array操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.导入numpy库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.建立一个一维数组 a 初始化为[4,5,6], (1)输出a 的类型（type）(2)输出a的各维度的大小（shape）(3)输出 a的第一个元素（值为4）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dtype of a: int32\n",
      "Shape of a: (3,)\n",
      "a[0]: 4\n"
     ]
    }
   ],
   "source": [
    "a=np.array([4,5,6])\n",
    "print(\"Dtype of a:\",a.dtype)\n",
    "print(\"Shape of a:\",a.shape)\n",
    "print(\"a[0]:\",a[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.建立一个二维数组 b,初始化为 [ [4, 5, 6],[1, 2, 3]] (1)输出各维度的大小（shape）(2)输出 b(0,0)，b(0,1),b(1,1) 这三个元素（对应值分别为4,5,2）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of b: (2, 3)\n",
      "Elements: 4 5 2\n"
     ]
    }
   ],
   "source": [
    "b = np.array([[4, 5, 6], [1, 2, 3]])\n",
    "print(\"Shape of b:\", b.shape)\n",
    "print(\"Elements:\", b[0, 0], b[0, 1], b[1, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.  (1)建立一个全0矩阵 a, 大小为 3x3; 类型为整型（提示: dtype = int）(2)建立一个全1矩阵b,大小为4x5;  (3)建立一个单位矩阵c ,大小为4x4; (4)生成一个随机数矩阵d,大小为 3x2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a (3x3 zeros):\n",
      " [[0 0 0]\n",
      " [0 0 0]\n",
      " [0 0 0]]\n",
      "b (4x5 ones):\n",
      " [[1. 1. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1.]]\n",
      "c (4x4 identity):\n",
      " [[1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [0. 0. 0. 1.]]\n",
      "d (3x2 random):\n",
      " [[0.64468573 0.14629355]\n",
      " [0.65767129 0.91443191]\n",
      " [0.83509673 0.40928183]]\n"
     ]
    }
   ],
   "source": [
    "a = np.zeros((3, 3), dtype=int)\n",
    "b = np.ones((4, 5))\n",
    "c = np.eye(4)\n",
    "d = np.random.rand(3, 2)\n",
    "print(\"a (3x3 zeros):\\n\", a)\n",
    "print(\"b (4x5 ones):\\n\", b)\n",
    "print(\"c (4x4 identity):\\n\", c)\n",
    "print(\"d (3x2 random):\\n\", d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. 建立一个数组 a,(值为[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] ) ,(1)打印a; (2)输出  下标为(2,3),(0,0) 这两个数组元素的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a:\n",
      " [[ 1  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n",
      "Elements (2,3) and (0,0): 12 1\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])\n",
    "print(\"a:\\n\", a)\n",
    "print(\"Elements (2,3) and (0,0):\", a[2, 3], a[0, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.把上一题的 a数组的 0到1行 2到3列，放到b里面去，（此处不需要从新建立a,直接调用即可）(1),输出b;(2) 输出b 的（0,0）这个元素的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subarray b:\n",
      " [[3 4]\n",
      " [7 8]]\n",
      "b[0,0]: 3\n"
     ]
    }
   ],
   "source": [
    "b = a[0:2, 2:4]\n",
    "print(\"Subarray b:\\n\", b)\n",
    "print(\"b[0,0]:\", b[0, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " #### 7. 把第5题中数组a的最后两行所有元素放到 c中，（提示： a[1:2, :]）(1)输出 c ; (2) 输出 c 中第一行的最后一个元素（提示，使用 -1                 表示最后一个元素）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subarray c:\n",
      " [[ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n",
      "First row last element of c: 8\n"
     ]
    }
   ],
   "source": [
    "c = a[1:, :]\n",
    "print(\"Subarray c:\\n\", c)\n",
    "print(\"First row last element of c:\", c[0, -1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8.建立数组a,初始化a为[[1, 2], [3, 4], [5, 6]]，输出 （0,0）（1,1）（2,0）这三个元素（提示： 使用 print(a[[0, 1, 2], [0, 1, 0]]) ）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected elements: [1 4 5]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1, 2], [3, 4], [5, 6]])\n",
    "print(\"Selected elements:\", a[[0, 1, 2], [0, 1, 0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9.建立矩阵a ,初始化为[[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]，输出(0,0),(1,2),(2,0),(3,1) (提示使用 b = np.array([0, 2, 0, 1])                     print(a[np.arange(4), b]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected elements: [ 1  6  7 11]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])\n",
    "b = np.array([0, 2, 0, 1])\n",
    "print(\"Selected elements:\", a[np.arange(4), b])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10.对9 中输出的那四个元素，每个都加上10，然后重新输出矩阵a.(提示： a[np.arange(4), b] += 10 ）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modified a:\n",
      " [[11  2  3]\n",
      " [ 4  5 16]\n",
      " [17  8  9]\n",
      " [10 21 12]]\n"
     ]
    }
   ],
   "source": [
    "a[np.arange(4), b] += 10\n",
    "print(\"Modified a:\\n\", a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### array 的数学运算"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 11.  执行 x = np.array([1, 2])，然后输出 x 的数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data type of x: int32\n"
     ]
    }
   ],
   "source": [
    "x = np.array([1, 2])\n",
    "print(\"Data type of x:\", x.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 12.执行 x = np.array([1.0, 2.0]) ，然后输出 x 的数据类类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data type of x: float64\n"
     ]
    }
   ],
   "source": [
    "x = np.array([1.0, 2.0])\n",
    "print(\"Data type of x:\", x.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 13.执行 x = np.array([[1, 2], [3, 4]], dtype=np.float64) ，y = np.array([[5, 6], [7, 8]], dtype=np.float64)，然后输出 x+y ,和 np.add(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x + y:\n",
      " [[ 6.  8.]\n",
      " [10. 12.]]\n",
      "np.add(x, y):\n",
      " [[ 6.  8.]\n",
      " [10. 12.]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1, 2], [3, 4]], dtype=np.float64)\n",
    "y = np.array([[5, 6], [7, 8]], dtype=np.float64)\n",
    "print(\"x + y:\\n\", x + y)\n",
    "print(\"np.add(x, y):\\n\", np.add(x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 14. 利用 13题目中的x,y 输出 x-y 和 np.subtract(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x - y:\n",
      " [[-4. -4.]\n",
      " [-4. -4.]]\n",
      "np.subtract(x, y):\n",
      " [[-4. -4.]\n",
      " [-4. -4.]]\n"
     ]
    }
   ],
   "source": [
    "print(\"x - y:\\n\", x - y)\n",
    "print(\"np.subtract(x, y):\\n\", np.subtract(x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 15. 利用13题目中的x，y 输出 x*y ,和 np.multiply(x, y) 还有  np.dot(x,y),比较差异。然后自己换一个不是方阵的试试。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Element-wise multiplication:\n",
      " [[ 5. 12.]\n",
      " [21. 32.]]\n",
      "np.multiply(x, y):\n",
      " [[ 5. 12.]\n",
      " [21. 32.]]\n",
      "Matrix multiplication:\n",
      " [[19. 22.]\n",
      " [43. 50.]]\n",
      "Matrix multiplication:\n",
      " [[ 4.54007058  5.33104986  6.12202914  6.91300842]\n",
      " [11.51824364 13.09034684 14.66245004 16.23455323]\n",
      " [ 7.85902008  9.10339864 10.3477772  11.59215575]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Element-wise multiplication:\\n\", x * y)\n",
    "print(\"np.multiply(x, y):\\n\", np.multiply(x, y))\n",
    "print(\"Matrix multiplication:\\n\", np.dot(x, y))\n",
    "print(\"Matrix multiplication:\\n\", np.dot(d, c))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 16. 利用13题目中的x,y,输出 x / y .(提示 ： 使用函数 np.divide())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x / y:\n",
      " [[0.2        0.33333333]\n",
      " [0.42857143 0.5       ]]\n"
     ]
    }
   ],
   "source": [
    "print(\"x / y:\\n\", np.divide(x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 17. 利用13题目中的x,输出 x的 开方。(提示： 使用函数 np.sqrt() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqrt(x):\n",
      " [[1.         1.41421356]\n",
      " [1.73205081 2.        ]]\n"
     ]
    }
   ],
   "source": [
    "print(\"sqrt(x):\\n\", np.sqrt(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 18.利用13题目中的x,y ,执行 print(x.dot(y)) 和 print(np.dot(x,y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x.dot(y):\n",
      " [[19. 22.]\n",
      " [43. 50.]]\n",
      "np.dot(x, y):\n",
      " [[19. 22.]\n",
      " [43. 50.]]\n"
     ]
    }
   ],
   "source": [
    "print(\"x.dot(y):\\n\", x.dot(y))\n",
    "print(\"np.dot(x, y):\\n\", np.dot(x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 19.利用13题目中的 x,进行求和。提示：输出三种求和 (1)print(np.sum(x)):   (2)print(np.sum(x，axis =0 ));   (3)print(np.sum(x,axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum all: 10.0\n",
      "Sum axis=0: [4. 6.]\n",
      "Sum axis=1: [3. 7.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Sum all:\", np.sum(x))\n",
    "print(\"Sum axis=0:\", np.sum(x, axis=0))\n",
    "print(\"Sum axis=1:\", np.sum(x, axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 20.利用13题目中的 x,进行求平均数（提示：输出三种平均数(1)print(np.mean(x)) (2)print(np.mean(x,axis = 0))(3) print(np.mean(x,axis =1))）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean all: 2.5\n",
      "Mean axis=0: [2. 3.]\n",
      "Mean axis=1: [1.5 3.5]\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean all:\", np.mean(x))\n",
    "print(\"Mean axis=0:\", np.mean(x, axis=0))\n",
    "print(\"Mean axis=1:\", np.mean(x, axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 21.利用13题目中的x，对x 进行矩阵转置，然后输出转置后的结果，（提示： x.T 表示对 x 的转置）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transpose of x:\n",
      " [[1. 3.]\n",
      " [2. 4.]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Transpose of x:\\n\", x.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "source": [
    "#### 22.利用13题目中的x,求e的指数（提示： 函数 np.exp()）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exp(x):\n",
      " [[ 2.71828183  7.3890561 ]\n",
      " [20.08553692 54.59815003]]\n"
     ]
    }
   ],
   "source": [
    "print(\"exp(x):\\n\", np.exp(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 23.利用13题目中的 x,求值最大的下标（提示(1)print(np.argmax(x)) ,(2) print(np.argmax(x, axis =0))(3)print(np.argmax(x),axis =1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Argmax (global): 3\n",
      "Argmax (axis=0): [1 1]\n",
      "Argmax (axis=1): [1 1]\n"
     ]
    }
   ],
   "source": [
    "print(\"Argmax (global):\", np.argmax(x))\n",
    "print(\"Argmax (axis=0):\", np.argmax(x, axis=0))\n",
    "print(\"Argmax (axis=1):\", np.argmax(x, axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 24,画图，y=x*x 其中 x = np.arange(0, 100, 0.1) （提示这里用到  matplotlib.pyplot 库）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = np.arange(0, 100, 0.1)\n",
    "y = x ** 2\n",
    "plt.plot(x, y)\n",
    "plt.title(\"y = x^2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 25.画图。画正弦函数和余弦函数， x = np.arange(0, 3 * np.pi, 0.1)(提示：这里用到 np.sin() np.cos() 函数和 matplotlib.pyplot 库)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 3 * np.pi, 0.1)\n",
    "y_sin = np.sin(x)\n",
    "y_cos = np.cos(x)\n",
    "plt.plot(x, y_sin, label=\"sin(x)\")\n",
    "plt.plot(x, y_cos, label=\"cos(x)\")\n",
    "plt.legend()\n",
    "plt.title(\"Sine and Cosine\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
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